The Entropic Bound for Transformers: Why Static Rank Fails and Attention-Native Rank Recovers

Researchers have proposed a new mathematical framework called the Entropic Bound to determine the minimum model capacity required for Transformers to complete specific tasks. By analyzing why static rank limitations hinder performance, the study suggests that attention-native rank allows models to scale more efficiently. This approach offers a potential theoretical foundation for predicting model requirements beyond traditional empirical scaling laws.